
"""simple docstring"""



import argparse

import pytorch_lightning as pl
import torch
from torch import nn

from transformers import LongformerForQuestionAnswering, LongformerModel





class 					lowerCAmelCase			(				pl.LightningModule					):
     """simple docstring"""







     def __init__(      self ,		UpperCamelCase__							)					->  List[str]:







            '''simple docstring'''





            super().__init__()
            lowerCamelCase_       							=		model
            lowerCamelCase_       							=		2
            lowerCamelCase_       							=		nn.Linear(self.model.config.hidden_size ,		self.num_labels							)




     def    _lowerCAmelCase		(      self							)					->  Tuple:







            '''simple docstring'''





            pass



def lowerCamelCase_				(     _lowerCamelCase			:	str					,				_lowerCamelCase			:	str					,				_lowerCamelCase			:	str				):
       # load longformer model from model identifier
       lowerCamelCase_       							=		LongformerModel.from_pretrained(_lowerCamelCase				)
       lowerCamelCase_       							=		LightningModel(_lowerCamelCase				)

       lowerCamelCase_       							=		torch.load(_lowerCamelCase					,				map_location=torch.device('''cpu'''				)				)
       lightning_model.load_state_dict(ckpt['''state_dict''']				)

       # init longformer question answering model
       lowerCamelCase_       							=		LongformerForQuestionAnswering.from_pretrained(_lowerCamelCase				)

       # transfer weights
       longformer_for_qa.longformer.load_state_dict(lightning_model.model.state_dict()				)
       longformer_for_qa.qa_outputs.load_state_dict(lightning_model.qa_outputs.state_dict()				)
       longformer_for_qa.eval()

       # save model
       longformer_for_qa.save_pretrained(_lowerCamelCase				)

       print(F"""Conversion successful. Model saved under {pytorch_dump_folder_path}"""				)


if __name__ == "__main__":
 __lowercase						:     Dict							      =		argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     """--longformer_model""",
     default=None,
     type=str,
     required=True,
     help="""model identifier of longformer. Should be either `longformer-base-4096` or `longformer-large-4096`.""",
 )
 parser.add_argument(
     """--longformer_question_answering_ckpt_path""",
     default=None,
     type=str,
     required=True,
     help="""Path the official PyTorch Lightning Checkpoint.""",
 )
 parser.add_argument(
     """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
 )
 __lowercase						:     List[str]							      =		parser.parse_args()
 convert_longformer_qa_checkpoint_to_pytorch(
     args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
 )
def 	lowerCamelCase					(	UpperCamelCase     :				int = 60_08_51_47_51_43							)   ->    int:

		try:
				_lowerCamelCase				   =  int(UpperCamelCase							)
		except (TypeError, ValueError):
				raise TypeError('Parameter n must be int or castable to int.'							)
		if n <= 0:
				raise ValueError('Parameter n must be greater than or equal to one.'							)
		_lowerCamelCase				   =  1
		_lowerCamelCase				   =  2
		while i * i <= n:
				while n % i == 0:
						_lowerCamelCase				   =  i
						n //= i
				i += 1
		if n > 1:
				_lowerCamelCase				   =  n
		return int(UpperCamelCase							)


if __name__ == "__main__":
				print(F'''{solution() = }''')